<![CDATA[In recent years, an increasing number of Indonesian youth have expressed a desire to seek better opportunities and life prospects abroad. One of the hashtags widely used to articulate this aspiration is #KaburDuluAja. This hashtag has emerged as an expression of emotions, anxiety, and even frustration among certain segments of society, particularly the younger generation, in response to the current social, economic, and political conditions in Indonesia. This study aims to comprehensively examine the phenomenon of public discourse on social media related to the #KaburDuluAja hashtag as a reflection of the potential for brain drain in Indonesia. To achieve this objective, a hybrid approach is employed, integrating Clustering, Association Rule Mining, and Sentiment Classification methods. The research adopts a quantitative approach supported by text mining techniques and social media data analysis. Furthermore, this study is both descriptive and predictive in nature. The research does not focus on a specific geographical location; rather, it examines public conversations on social media platforms, particularly Twitter (currently known as X). The data utilized in this study were collected through a web crawling process. The findings indicate that public conversations related to the #KaburDuluAja phenomenon can be categorized into five main clusters. The evaluation of several classification models reveals that the Naïve Bayes algorithm achieves the highest accuracy in predicting sentiment, reaching 99.85%. The K-Nearest Neighbor (KNN) model achieves an accuracy of 75.79%, while the Decision Tree and Random Forest models demonstrate relatively lower performance, with accuracy levels around 60%. The #KaburDuluAja phenomenon is not merely a form of humor or casual expression on social media; rather, it represents a genuine reflection of social unrest, particularly concerning employment opportunities, welfare conditions, and the future prospects of Indonesia’s younger generation.]]>
